Knowledge, attitudes, behaviours, and beliefs of healthcare provider students regarding mandatory influenza vaccination
Bibliographic record
Abstract
Influenza infection poses the same risk to healthcare students as to practising clinicians. While there is substantial dialog about the benefits, risks, and ethics of mandatory influenza immunization policies in Canada, there has been little engagement of healthcare students. To explore the knowledge, attitudes, beliefs, and behaviours of healthcare students, we administered a web-based survey to students at Dalhousie University. Influenza vaccination status varied by program type, with 86.3% of medical students (n = 124) and 52.4% of nursing students (n = 96) self-reporting receipt of the influenza vaccine both in the previous and current seasons; pharmacy students' coverage fell between the two. Pharmacy students had higher mean knowledge scores (10.0 out of 13 questions) than medical (9.26) and nursing (8.88) students. Between 56.1% and 64.5% of students across disciplines were in support of a mandatory masking or vaccination policy, and between 72.6% and 82.3% of students would comply if such a policy were in place. A sense of duty to be immunized, desire to be taught more about influenza and influenza vaccine, belief that the hospital has a right to know vaccination status, support for declination policy, and willingness to accept consequences of noncompliance were all predictors of student support of mandatory policies. Medical and pharmacy students tended to hold more pro-influenza vaccination attitudes, had higher knowledge scores, and better vaccine coverage than nursing students. Based on the overall vaccination behaviour, knowledge, beliefs, and attitudes of students surveyed, this study demonstrates that mandatory influenza immunization policies are generally supported by the next generation of practitioners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".